Understanding human behavior from complementary egocentric (ego) and exocentric (exo) points of view enables the development of systems that can support workers in industrial environments and enhance their safety. However, progress in this area is hindered by the lack of datasets capturing both views in realistic industrial scenarios. To address this gap, we propose ENIGMA-360, a new ego-exo dataset acquired in a real industrial scenario. The dataset is composed of 180 egocentric and 180 exocentric procedural videos temporally synchronized offering complementary information of the same scene. The 360 videos have been labeled with temporal and spatial annotations, enabling the study of different aspects of human behavior in industrial domain. We provide baseline experiments for 3 foundational tasks for human behavior understanding: 1) Temporal Action Segmentation, 2) Keystep Recognition and 3) Egocentric Human-Object Interaction Detection, showing the limits of state-of-the-art approaches on this challenging scenario. These results highlight the need for new models capable of robust ego-exo understanding in real-world environments. We publicly release the dataset and its annotations at https://fpv-iplab.github.io/ENIGMA-360/.
@article{arxiv.2603.09741,
title = {ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios},
author = {Francesco Ragusa and Rosario Leonardi and Michele Mazzamuto and Daniele Di Mauro and Camillo Quattrocchi and Alessandro Passanisi and Irene D'Ambra and Antonino Furnari and Giovanni Maria Farinella},
journal= {arXiv preprint arXiv:2603.09741},
year = {2026}
}